Technology to improve imaging has been integrated into increasing numbers of applications recently. The technology takes shape in a variety of forms across these applications, with some imaging technology being implemented via computer software and other imaging technology resulting from the physical optics used. In digital camera technology, for example, optical lenses with reduced chromatic aberration and reduced focusing errors are used to improve image quality. Image sensors with high pixel counts are used to offer better image resolution and capture more detail within an image. In military applications, imaging systems are used for guidance systems to help identify objects from often times great distances and limited visibility. In manufacturing applications, computer imaging technology is used as part of a machine vision system, for example, where part machining is automated based on images recorded during tooling.
Although relatively effective in certain applications, imaging technology is limited. One notable limitation occurs in digital imaging systems, which are sorely limited by pixel size and count. Take for example consumer applications, e.g., consumer quality digital cameras where sizeable increases in digital camera pixel count have occurred recently. Yet, these increases are not sufficient enough to use in high deemed imaging systems, especially those that would be called upon to produce small scale structures, such as those with micron sized features.
Machine vision technology, used to image many small-scale structures, requires very high resolution imaging. But limitations in the photodetector or CCD camera used for imaging limit that resolution, even when the optics used are capable of higher resolution. With resolutions higher than those used today, manufacturers could produce smaller, more intricate structures, and manufacturers would be able to more easily identify device defects.
One limitation in machine vision technology is with the requirement of a quantization of the physical space occupied by the machined component—navigated by the tooling machine—to a pixel grid on an image sensor. If the pixel grid on the image sensor is not appropriately mapped to the physical space of the image or the machine tool, then errors will occur. For example, an edge of the tooled component might overlap a number of pixels in the pixel grid of the image sensor, thereby making exact edge resolution difficult. Thus, although ideally a device's edge would match up perfectly on a row or column of pixels, mapping of the pixel grid to the tooled component is not that exact. The edge of the component may cast an image over a number of adjacent pixels, each responding slightly different to the amount of light impending upon it. This phenomena of poor mapping could limit a microprocessor's ability to resolve the image cast on those pixels to determine the exact position of the tool in the physical space—the machine vision system measures light data on the pixel grid to guide machine tooling in the physical space.
To improve pixel resolution in imaging systems, resolution enhancing techniques have been developed. Super resolution techniques overcome some of the pixel resolution problems by comparing numerous lower resolution images to one another and then statistically developing a higher resolution image from these lower resolution ones. The lower resolution images are all slightly offset from one another and then compared to one another. Super resolution techniques interpret grayscale patterns of an image by comparing these slightly shifted lower resolution images.
In application, super resolution techniques use cameras to capture a sequence of frames of known sub-pixel shift from a reference image. These frames are registered, that is, analyzed by removing the image shift. Then the registered images are compared to derive edge statistical data, which is used to derive a sharper, higher resolution edge.
Although super resolution techniques can offer some image enhancement, there are drawbacks to these techniques that prevent them from widespread use. One, it is quite difficult and time-consuming to capture sub-pixel shifted frames. Expensive translation stages that can be adjusted a sub-pixel amount are used to shift the component being imaged. For example, the component is mounted to the translation stage and the stages are moved by small amounts as the sub-pixel shifted frames are captured. These stages must have both X-translation mechanisms and Y-translation mechanisms. The translation stages may be required to bear varying loads and have different inertial masses that must be overcome prior to movement the stage. Two, the size of these translation stages limits the extent to which a compact super resolution device can be made.
The applicants have found that it is desirable to have a technique improving resolution of an image, for example, to allow for super resolution, and do so in a faster, more cost effective manner.